Overview

Dataset statistics

Number of variables24
Number of observations1.639
Missing cells896
Missing cells (%)2.3%
Duplicate rows151
Duplicate rows (%)9.2%
Total size in memory307.4 KiB
Average record size in memory192.1 B

Variable types

Text2
DateTime1
Categorical4
Numeric14
Boolean3

Alerts

Dataset has 151 (9.2%) duplicate rowsDuplicates
Abdominal Circumference (cm) is highly overall correlated with Waist-to-Height RatioHigh correlation
BMI is highly overall correlated with CVD Risk Score and 1 other fieldsHigh correlation
CVD Risk Score is highly overall correlated with BMIHigh correlation
Estimated LDL (mg/dL) is highly overall correlated with Total Cholesterol (mg/dL)High correlation
Height (cm) is highly overall correlated with Height (m)High correlation
Height (m) is highly overall correlated with Height (cm)High correlation
Total Cholesterol (mg/dL) is highly overall correlated with Estimated LDL (mg/dL)High correlation
Waist-to-Height Ratio is highly overall correlated with Abdominal Circumference (cm)High correlation
Weight (kg) is highly overall correlated with BMIHigh correlation
Age has 68 (4.1%) missing valuesMissing
Weight (kg) has 73 (4.5%) missing valuesMissing
Height (m) has 61 (3.7%) missing valuesMissing
BMI has 53 (3.2%) missing valuesMissing
Abdominal Circumference (cm) has 61 (3.7%) missing valuesMissing
Total Cholesterol (mg/dL) has 68 (4.1%) missing valuesMissing
HDL (mg/dL) has 82 (5.0%) missing valuesMissing
Fasting Blood Sugar (mg/dL) has 54 (3.3%) missing valuesMissing
Height (cm) has 68 (4.1%) missing valuesMissing
Waist-to-Height Ratio has 76 (4.6%) missing valuesMissing
Systolic BP has 61 (3.7%) missing valuesMissing
Diastolic BP has 85 (5.2%) missing valuesMissing
Estimated LDL (mg/dL) has 57 (3.5%) missing valuesMissing
CVD Risk Score has 29 (1.8%) missing valuesMissing

Reproduction

Analysis started2026-02-28 19:48:33.608444
Analysis finished2026-02-28 19:48:42.981601
Duration9.37 seconds
Software versionydata-profiling vv4.18.1
Download configurationconfig.json

Variables

Distinct1376
Distinct (%)84.0%
Missing0
Missing (%)0.0%
Memory size12.9 KiB
2026-02-28T14:48:43.055148image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length8
Median length8
Mean length8
Min length8

Characters and Unicode

Total characters13.112
Distinct characters62
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1.205 ?
Unique (%)73.5%

Sample

1st rowisDx5313
2nd rowLHCK2961
3rd rowWjVn1699
4th rowdCDO1109
5th rowpnpE1080
ValueCountFrequency (%)
ahyt13463
 
0.2%
rlsb85093
 
0.2%
rwgu56473
 
0.2%
djuc50843
 
0.2%
ylce29263
 
0.2%
dsiv49493
 
0.2%
pepz90343
 
0.2%
stpp58103
 
0.2%
dhuj72393
 
0.2%
gdbf96553
 
0.2%
Other values (1366)1609
98.2%
2026-02-28T14:48:43.223591image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
4670
 
5.1%
0670
 
5.1%
7670
 
5.1%
9668
 
5.1%
1668
 
5.1%
6664
 
5.1%
5653
 
5.0%
2651
 
5.0%
8641
 
4.9%
3601
 
4.6%
Other values (52)6556
50.0%

Most occurring categories

ValueCountFrequency (%)
(unknown)13112
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
4670
 
5.1%
0670
 
5.1%
7670
 
5.1%
9668
 
5.1%
1668
 
5.1%
6664
 
5.1%
5653
 
5.0%
2651
 
5.0%
8641
 
4.9%
3601
 
4.6%
Other values (52)6556
50.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown)13112
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
4670
 
5.1%
0670
 
5.1%
7670
 
5.1%
9668
 
5.1%
1668
 
5.1%
6664
 
5.1%
5653
 
5.0%
2651
 
5.0%
8641
 
4.9%
3601
 
4.6%
Other values (52)6556
50.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown)13112
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
4670
 
5.1%
0670
 
5.1%
7670
 
5.1%
9668
 
5.1%
1668
 
5.1%
6664
 
5.1%
5653
 
5.0%
2651
 
5.0%
8641
 
4.9%
3601
 
4.6%
Other values (52)6556
50.0%
Distinct1002
Distinct (%)61.1%
Missing0
Missing (%)0.0%
Memory size12.9 KiB
Minimum2020-01-02 00:00:00
Maximum2025-12-30 00:00:00
Invalid dates0
Invalid dates (%)0.0%
2026-02-28T14:48:43.391621image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-28T14:48:43.444839image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

Sex
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size12.9 KiB
M
821 
F
818 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1.639
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowM
2nd rowF
3rd rowF
4th rowF
5th rowF

Common Values

ValueCountFrequency (%)
M821
50.1%
F818
49.9%

Length

2026-02-28T14:48:43.496860image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2026-02-28T14:48:43.523072image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
m821
50.1%
f818
49.9%

Most occurring characters

ValueCountFrequency (%)
M821
50.1%
F818
49.9%

Most occurring categories

ValueCountFrequency (%)
(unknown)1639
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
M821
50.1%
F818
49.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown)1639
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
M821
50.1%
F818
49.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown)1639
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
M821
50.1%
F818
49.9%

Age
Real number (ℝ)

Missing 

Distinct66
Distinct (%)4.2%
Missing68
Missing (%)4.1%
Infinite0
Infinite (%)0.0%
Mean46.803186
Minimum6.134
Maximum89.42
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size12.9 KiB
2026-02-28T14:48:43.557299image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum6.134
5-th percentile30
Q137
median46
Q355
95-th percentile72
Maximum89.42
Range83.286
Interquartile range (IQR)18

Descriptive statistics

Standard deviation13.039479
Coefficient of variation (CV)0.27860237
Kurtosis0.12462961
Mean46.803186
Median Absolute Deviation (MAD)9
Skewness0.39411053
Sum73527.805
Variance170.02801
MonotonicityNot monotonic
2026-02-28T14:48:43.599252image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3755
 
3.4%
3354
 
3.3%
3152
 
3.2%
3251
 
3.1%
4950
 
3.1%
4849
 
3.0%
3049
 
3.0%
4647
 
2.9%
3847
 
2.9%
5144
 
2.7%
Other values (56)1073
65.5%
(Missing)68
 
4.1%
ValueCountFrequency (%)
6.1341
 
0.1%
6.423
 
0.2%
6.993
 
0.2%
7.0251
 
0.1%
8.0381
 
0.1%
9.3761
 
0.1%
2510
0.6%
2614
0.9%
279
0.5%
288
0.5%
ValueCountFrequency (%)
89.421
 
0.1%
89.1621
 
0.1%
88.4641
 
0.1%
85.7151
 
0.1%
85.2411
 
0.1%
7918
1.1%
787
 
0.4%
773
 
0.2%
7613
0.8%
7516
1.0%

Weight (kg)
Real number (ℝ)

High correlation  Missing 

Distinct944
Distinct (%)60.3%
Missing73
Missing (%)4.5%
Infinite0
Infinite (%)0.0%
Mean85.666006
Minimum13.261
Maximum158.523
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size12.9 KiB
2026-02-28T14:48:43.640880image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum13.261
5-th percentile53.383
Q167.1
median86.314
Q3104.8015
95-th percentile117.1765
Maximum158.523
Range145.262
Interquartile range (IQR)37.7015

Descriptive statistics

Standard deviation21.712504
Coefficient of variation (CV)0.25345531
Kurtosis-0.75101892
Mean85.666006
Median Absolute Deviation (MAD)18.914
Skewness-0.077092653
Sum134152.96
Variance471.43283
MonotonicityNot monotonic
2026-02-28T14:48:43.685637image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
117.87
 
0.4%
72.87
 
0.4%
97.87
 
0.4%
100.46
 
0.4%
73.36
 
0.4%
116.26
 
0.4%
118.36
 
0.4%
74.76
 
0.4%
665
 
0.3%
595
 
0.3%
Other values (934)1505
91.8%
(Missing)73
 
4.5%
ValueCountFrequency (%)
13.2611
 
0.1%
15.0362
0.1%
19.5783
0.2%
21.0381
 
0.1%
21.3161
 
0.1%
50.14
0.2%
50.21
 
0.1%
50.3071
 
0.1%
50.3431
 
0.1%
50.43
0.2%
ValueCountFrequency (%)
158.5231
0.1%
157.1641
0.1%
153.5561
0.1%
149.8771
0.1%
149.3351
0.1%
1201
0.1%
119.91
0.1%
119.8061
0.1%
119.81
0.1%
119.71
0.1%

Height (m)
Real number (ℝ)

High correlation  Missing 

Distinct332
Distinct (%)21.0%
Missing61
Missing (%)3.7%
Infinite0
Infinite (%)0.0%
Mean1.7574385
Minimum1.371
Maximum2.146
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size12.9 KiB
2026-02-28T14:48:43.730705image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1.371
5-th percentile1.5757
Q11.6665
median1.76
Q31.85
95-th percentile1.94645
Maximum2.146
Range0.775
Interquartile range (IQR)0.1835

Descriptive statistics

Standard deviation0.11801174
Coefficient of variation (CV)0.067149855
Kurtosis-0.21590341
Mean1.7574385
Median Absolute Deviation (MAD)0.09
Skewness0.051690986
Sum2773.238
Variance0.013926771
MonotonicityNot monotonic
2026-02-28T14:48:43.779913image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.8146
 
2.8%
1.8645
 
2.7%
1.6345
 
2.7%
1.7443
 
2.6%
1.7643
 
2.6%
1.6642
 
2.6%
1.6142
 
2.6%
1.8940
 
2.4%
1.738
 
2.3%
1.8837
 
2.3%
Other values (322)1157
70.6%
(Missing)61
 
3.7%
ValueCountFrequency (%)
1.3711
 
0.1%
1.381
 
0.1%
1.3881
 
0.1%
1.411
 
0.1%
1.5021
 
0.1%
1.5031
 
0.1%
1.5051
 
0.1%
1.5063
0.2%
1.5072
0.1%
1.5081
 
0.1%
ValueCountFrequency (%)
2.1463
0.2%
2.1411
 
0.1%
2.1391
 
0.1%
2.1231
 
0.1%
2.1171
 
0.1%
2.1131
 
0.1%
2.111
 
0.1%
2.1091
 
0.1%
21
 
0.1%
1.9982
0.1%

BMI
Real number (ℝ)

High correlation  Missing 

Distinct737
Distinct (%)46.5%
Missing53
Missing (%)3.2%
Infinite0
Infinite (%)0.0%
Mean28.424744
Minimum4.317
Maximum53.028
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size12.9 KiB
2026-02-28T14:48:43.825532image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum4.317
5-th percentile17.8
Q122.6
median28
Q333.963
95-th percentile39.78325
Maximum53.028
Range48.711
Interquartile range (IQR)11.363

Descriptive statistics

Standard deviation7.3092747
Coefficient of variation (CV)0.25714479
Kurtosis-0.3505387
Mean28.424744
Median Absolute Deviation (MAD)5.7425
Skewness0.15108189
Sum45081.644
Variance53.425497
MonotonicityNot monotonic
2026-02-28T14:48:43.869543image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
33.412
 
0.7%
19.511
 
0.7%
19.711
 
0.7%
23.710
 
0.6%
38.210
 
0.6%
27.110
 
0.6%
23.19
 
0.5%
25.59
 
0.5%
16.69
 
0.5%
20.89
 
0.5%
Other values (727)1486
90.7%
(Missing)53
 
3.2%
ValueCountFrequency (%)
4.3171
 
0.1%
5.1843
0.2%
6.2352
0.1%
7.1051
 
0.1%
152
0.1%
15.11
 
0.1%
15.31
 
0.1%
15.42
0.1%
15.52
0.1%
15.61
 
0.1%
ValueCountFrequency (%)
53.0281
0.1%
52.741
0.1%
52.1921
0.1%
52.1361
0.1%
51.9841
0.1%
51.4021
0.1%
51.0221
0.1%
46.21
0.1%
46.11
0.1%
45.61
0.1%

Abdominal Circumference (cm)
Real number (ℝ)

High correlation  Missing 

Distinct810
Distinct (%)51.3%
Missing61
Missing (%)3.7%
Infinite0
Infinite (%)0.0%
Mean91.538861
Minimum49.542
Maximum136.336
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size12.9 KiB
2026-02-28T14:48:43.913130image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum49.542
5-th percentile72.1
Q179.7
median91.2
Q3102.26725
95-th percentile113.04375
Maximum136.336
Range86.794
Interquartile range (IQR)22.56725

Descriptive statistics

Standard deviation13.427985
Coefficient of variation (CV)0.14669164
Kurtosis-0.5422464
Mean91.538861
Median Absolute Deviation (MAD)11.2415
Skewness0.21239507
Sum144448.32
Variance180.31079
MonotonicityNot monotonic
2026-02-28T14:48:43.959720image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
86.911
 
0.7%
78.110
 
0.6%
94.49
 
0.5%
76.39
 
0.5%
99.18
 
0.5%
98.28
 
0.5%
96.68
 
0.5%
79.77
 
0.4%
89.17
 
0.4%
74.67
 
0.4%
Other values (800)1494
91.2%
(Missing)61
 
3.7%
ValueCountFrequency (%)
49.5423
0.2%
53.0021
 
0.1%
704
0.2%
70.0911
 
0.1%
70.12
0.1%
70.1841
 
0.1%
70.22
0.1%
70.33
0.2%
70.3331
 
0.1%
70.41
 
0.1%
ValueCountFrequency (%)
136.3361
0.1%
136.3191
0.1%
134.2971
0.1%
133.8461
0.1%
133.7351
0.1%
133.0651
0.1%
132.8611
0.1%
132.0451
0.1%
130.3711
0.1%
119.9961
0.1%
Distinct1152
Distinct (%)70.3%
Missing0
Missing (%)0.0%
Memory size12.9 KiB
2026-02-28T14:48:44.058009image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length7
Median length6
Mean length5.9865772
Min length5

Characters and Unicode

Total characters9.812
Distinct characters11
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique846 ?
Unique (%)51.6%

Sample

1st row112/83
2nd row101/91
3rd row90/74
4th row92/89
5th row121/68
ValueCountFrequency (%)
124/726
 
0.4%
127/846
 
0.4%
121/685
 
0.3%
129/615
 
0.3%
119/884
 
0.2%
97/714
 
0.2%
144/704
 
0.2%
114/634
 
0.2%
111/764
 
0.2%
148/984
 
0.2%
Other values (1142)1593
97.2%
2026-02-28T14:48:44.207055image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
12353
24.0%
/1639
16.7%
9916
 
9.3%
7761
 
7.8%
6759
 
7.7%
0661
 
6.7%
8653
 
6.7%
2583
 
5.9%
4544
 
5.5%
3534
 
5.4%

Most occurring categories

ValueCountFrequency (%)
(unknown)9812
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
12353
24.0%
/1639
16.7%
9916
 
9.3%
7761
 
7.8%
6759
 
7.7%
0661
 
6.7%
8653
 
6.7%
2583
 
5.9%
4544
 
5.5%
3534
 
5.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown)9812
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
12353
24.0%
/1639
16.7%
9916
 
9.3%
7761
 
7.8%
6759
 
7.7%
0661
 
6.7%
8653
 
6.7%
2583
 
5.9%
4544
 
5.5%
3534
 
5.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown)9812
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
12353
24.0%
/1639
16.7%
9916
 
9.3%
7761
 
7.8%
6759
 
7.7%
0661
 
6.7%
8653
 
6.7%
2583
 
5.9%
4544
 
5.5%
3534
 
5.4%

Total Cholesterol (mg/dL)
Real number (ℝ)

High correlation  Missing 

Distinct211
Distinct (%)13.4%
Missing68
Missing (%)4.1%
Infinite0
Infinite (%)0.0%
Mean199.04367
Minimum-1.256
Maximum385.679
Zeros0
Zeros (%)0.0%
Negative1
Negative (%)0.1%
Memory size12.9 KiB
2026-02-28T14:48:44.250091image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum-1.256
5-th percentile109
Q1150
median199
Q3250
95-th percentile290
Maximum385.679
Range386.935
Interquartile range (IQR)100

Descriptive statistics

Standard deviation59.38867
Coefficient of variation (CV)0.29837005
Kurtosis-0.69406263
Mean199.04367
Median Absolute Deviation (MAD)50
Skewness-0.071375244
Sum312697.61
Variance3527.0141
MonotonicityNot monotonic
2026-02-28T14:48:44.296158image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
12716
 
1.0%
16015
 
0.9%
29615
 
0.9%
10915
 
0.9%
24114
 
0.9%
25214
 
0.9%
13213
 
0.8%
20613
 
0.8%
22813
 
0.8%
17913
 
0.8%
Other values (201)1430
87.2%
(Missing)68
 
4.1%
ValueCountFrequency (%)
-1.2561
 
0.1%
1.8171
 
0.1%
4.6131
 
0.1%
8.4981
 
0.1%
16.0881
 
0.1%
19.9321
 
0.1%
21.6623
 
0.2%
1008
0.5%
1019
0.5%
1024
0.2%
ValueCountFrequency (%)
385.6791
 
0.1%
379.441
 
0.1%
375.8141
 
0.1%
3007
0.4%
2994
 
0.2%
2988
0.5%
2974
 
0.2%
29615
0.9%
29513
0.8%
2946
 
0.4%

HDL (mg/dL)
Real number (ℝ)

Missing 

Distinct70
Distinct (%)4.5%
Missing82
Missing (%)5.0%
Infinite0
Infinite (%)0.0%
Mean56.183558
Minimum0.008
Maximum110.315
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size12.9 KiB
2026-02-28T14:48:44.345417image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0.008
5-th percentile32
Q142
median56
Q370
95-th percentile82
Maximum110.315
Range110.307
Interquartile range (IQR)28

Descriptive statistics

Standard deviation16.721702
Coefficient of variation (CV)0.29762626
Kurtosis-0.52157158
Mean56.183558
Median Absolute Deviation (MAD)14
Skewness0.0085811335
Sum87477.8
Variance279.61532
MonotonicityNot monotonic
2026-02-28T14:48:44.390296image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4444
 
2.7%
4641
 
2.5%
7837
 
2.3%
3836
 
2.2%
3533
 
2.0%
7933
 
2.0%
7333
 
2.0%
3133
 
2.0%
5233
 
2.0%
6132
 
2.0%
Other values (60)1202
73.3%
(Missing)82
 
5.0%
ValueCountFrequency (%)
0.0081
 
0.1%
0.6123
 
0.2%
0.9151
 
0.1%
1.2761
 
0.1%
6.2831
 
0.1%
6.8091
 
0.1%
7.5421
 
0.1%
3030
1.8%
3133
2.0%
3230
1.8%
ValueCountFrequency (%)
110.3153
 
0.2%
108.3041
 
0.1%
104.8821
 
0.1%
899
0.5%
885
 
0.3%
8712
0.7%
865
 
0.3%
857
0.4%
849
0.5%
8314
0.9%

Fasting Blood Sugar (mg/dL)
Real number (ℝ)

Missing 

Distinct142
Distinct (%)9.0%
Missing54
Missing (%)3.3%
Infinite0
Infinite (%)0.0%
Mean117.83686
Minimum15.306
Maximum219.667
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size12.9 KiB
2026-02-28T14:48:44.435413image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum15.306
5-th percentile74
Q192
median115
Q3139
95-th percentile178
Maximum219.667
Range204.361
Interquartile range (IQR)47

Descriptive statistics

Standard deviation32.379634
Coefficient of variation (CV)0.27478358
Kurtosis0.18548171
Mean117.83686
Median Absolute Deviation (MAD)23
Skewness0.50390118
Sum186771.42
Variance1048.4407
MonotonicityNot monotonic
2026-02-28T14:48:44.482692image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10327
 
1.6%
9626
 
1.6%
11526
 
1.6%
12425
 
1.5%
9224
 
1.5%
9024
 
1.5%
8523
 
1.4%
12023
 
1.4%
7023
 
1.4%
10722
 
1.3%
Other values (132)1342
81.9%
(Missing)54
 
3.3%
ValueCountFrequency (%)
15.3061
 
0.1%
15.6052
 
0.1%
18.961
 
0.1%
19.0141
 
0.1%
21.2111
 
0.1%
23.8171
 
0.1%
7023
1.4%
7113
0.8%
7215
0.9%
7318
1.1%
ValueCountFrequency (%)
219.6671
 
0.1%
219.1353
0.2%
218.0193
0.2%
215.6143
0.2%
213.6851
 
0.1%
212.9841
 
0.1%
212.3823
0.2%
209.1191
 
0.1%
1984
0.2%
1972
0.1%
Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size1.7 KiB
True
850 
False
789 
ValueCountFrequency (%)
True850
51.9%
False789
48.1%
2026-02-28T14:48:44.518699image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size1.7 KiB
False
821 
True
818 
ValueCountFrequency (%)
False821
50.1%
True818
49.9%
2026-02-28T14:48:44.536011image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Distinct3
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size12.9 KiB
High
582 
Moderate
537 
Low
520 

Length

Max length8
Median length4
Mean length4.9932886
Min length3

Characters and Unicode

Total characters8.184
Distinct characters13
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowHigh
2nd rowHigh
3rd rowHigh
4th rowModerate
5th rowLow

Common Values

ValueCountFrequency (%)
High582
35.5%
Moderate537
32.8%
Low520
31.7%

Length

2026-02-28T14:48:44.564592image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2026-02-28T14:48:44.588580image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
high582
35.5%
moderate537
32.8%
low520
31.7%

Most occurring characters

ValueCountFrequency (%)
e1074
13.1%
o1057
12.9%
g582
 
7.1%
i582
 
7.1%
H582
 
7.1%
h582
 
7.1%
M537
 
6.6%
d537
 
6.6%
r537
 
6.6%
a537
 
6.6%
Other values (3)1577
19.3%

Most occurring categories

ValueCountFrequency (%)
(unknown)8184
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e1074
13.1%
o1057
12.9%
g582
 
7.1%
i582
 
7.1%
H582
 
7.1%
h582
 
7.1%
M537
 
6.6%
d537
 
6.6%
r537
 
6.6%
a537
 
6.6%
Other values (3)1577
19.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown)8184
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e1074
13.1%
o1057
12.9%
g582
 
7.1%
i582
 
7.1%
H582
 
7.1%
h582
 
7.1%
M537
 
6.6%
d537
 
6.6%
r537
 
6.6%
a537
 
6.6%
Other values (3)1577
19.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown)8184
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e1074
13.1%
o1057
12.9%
g582
 
7.1%
i582
 
7.1%
H582
 
7.1%
h582
 
7.1%
M537
 
6.6%
d537
 
6.6%
r537
 
6.6%
a537
 
6.6%
Other values (3)1577
19.3%
Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size1.7 KiB
False
820 
True
819 
ValueCountFrequency (%)
False820
50.0%
True819
50.0%
2026-02-28T14:48:44.609829image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Height (cm)
Real number (ℝ)

High correlation  Missing 

Distinct506
Distinct (%)32.2%
Missing68
Missing (%)4.1%
Infinite0
Infinite (%)0.0%
Mean175.77008
Minimum136.498
Maximum214.394
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size12.9 KiB
2026-02-28T14:48:44.642148image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum136.498
5-th percentile157.8055
Q1167
median176
Q3185
95-th percentile194.9425
Maximum214.394
Range77.896
Interquartile range (IQR)18

Descriptive statistics

Standard deviation11.69588
Coefficient of variation (CV)0.066540787
Kurtosis-0.27447659
Mean175.77008
Median Absolute Deviation (MAD)9
Skewness0.051740503
Sum276134.8
Variance136.7936
MonotonicityNot monotonic
2026-02-28T14:48:44.688340image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
17644
 
2.7%
17443
 
2.6%
16343
 
2.6%
18143
 
2.6%
18842
 
2.6%
16141
 
2.5%
16641
 
2.5%
17039
 
2.4%
18638
 
2.3%
18938
 
2.3%
Other values (496)1159
70.7%
(Missing)68
 
4.1%
ValueCountFrequency (%)
136.4981
0.1%
139.2651
0.1%
141.4231
0.1%
1501
0.1%
150.1581
0.1%
150.2831
0.1%
150.2851
0.1%
150.6081
0.1%
150.6162
0.1%
150.7091
0.1%
ValueCountFrequency (%)
214.3941
 
0.1%
213.921
 
0.1%
213.1491
 
0.1%
211.1271
 
0.1%
210.9811
 
0.1%
210.6241
 
0.1%
210.5543
0.2%
210.2071
 
0.1%
199.961
 
0.1%
199.8211
 
0.1%

Waist-to-Height Ratio
Real number (ℝ)

High correlation  Missing 

Distinct337
Distinct (%)21.6%
Missing76
Missing (%)4.6%
Infinite0
Infinite (%)0.0%
Mean0.52244018
Minimum0.25
Maximum0.804
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size12.9 KiB
2026-02-28T14:48:44.737535image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0.25
5-th percentile0.4011
Q10.453
median0.519
Q30.582
95-th percentile0.6669
Maximum0.804
Range0.554
Interquartile range (IQR)0.129

Descriptive statistics

Standard deviation0.085692095
Coefficient of variation (CV)0.16402279
Kurtosis0.048155081
Mean0.52244018
Median Absolute Deviation (MAD)0.064
Skewness0.29148637
Sum816.574
Variance0.0073431352
MonotonicityNot monotonic
2026-02-28T14:48:44.781114image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.42317
 
1.0%
0.52213
 
0.8%
0.46613
 
0.8%
0.55213
 
0.8%
0.49913
 
0.8%
0.48112
 
0.7%
0.45312
 
0.7%
0.60112
 
0.7%
0.5512
 
0.7%
0.60711
 
0.7%
Other values (327)1435
87.6%
(Missing)76
 
4.6%
ValueCountFrequency (%)
0.251
 
0.1%
0.2593
0.2%
0.263
0.2%
0.2671
 
0.1%
0.2781
 
0.1%
0.361
 
0.1%
0.3621
 
0.1%
0.3651
 
0.1%
0.3661
 
0.1%
0.373
0.2%
ValueCountFrequency (%)
0.8042
0.1%
0.7872
0.1%
0.7851
 
0.1%
0.7841
 
0.1%
0.7831
 
0.1%
0.7821
 
0.1%
0.781
 
0.1%
0.7593
0.2%
0.7581
 
0.1%
0.7551
 
0.1%

Systolic BP
Real number (ℝ)

Missing 

Distinct100
Distinct (%)6.3%
Missing61
Missing (%)3.7%
Infinite0
Infinite (%)0.0%
Mean125.63264
Minimum49.914
Maximum202.711
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size12.9 KiB
2026-02-28T14:48:44.824655image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum49.914
5-th percentile93
Q1108
median125
Q3141
95-th percentile168
Maximum202.711
Range152.797
Interquartile range (IQR)33

Descriptive statistics

Standard deviation22.577463
Coefficient of variation (CV)0.17971017
Kurtosis-0.22099855
Mean125.63264
Median Absolute Deviation (MAD)17
Skewness0.31723454
Sum198248.3
Variance509.74184
MonotonicityNot monotonic
2026-02-28T14:48:44.869635image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10237
 
2.3%
13935
 
2.1%
11333
 
2.0%
11931
 
1.9%
13031
 
1.9%
14130
 
1.8%
12929
 
1.8%
11129
 
1.8%
11228
 
1.7%
12628
 
1.7%
Other values (90)1267
77.3%
(Missing)61
 
3.7%
ValueCountFrequency (%)
49.9141
 
0.1%
51.1481
 
0.1%
52.5271
 
0.1%
53.2381
 
0.1%
58.5131
 
0.1%
9016
1.0%
9122
1.3%
9226
1.6%
9318
1.1%
9420
1.2%
ValueCountFrequency (%)
202.7111
 
0.1%
198.1771
 
0.1%
197.4991
 
0.1%
194.1731
 
0.1%
192.4011
 
0.1%
1795
0.3%
1789
0.5%
1777
0.4%
1764
0.2%
1756
0.4%

Diastolic BP
Real number (ℝ)

Missing 

Distinct74
Distinct (%)4.8%
Missing85
Missing (%)5.2%
Infinite0
Infinite (%)0.0%
Mean82.887536
Minimum31.72
Maximum134.066
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size12.9 KiB
2026-02-28T14:48:44.919594image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum31.72
5-th percentile61
Q171
median82
Q393
95-th percentile112
Maximum134.066
Range102.346
Interquartile range (IQR)22

Descriptive statistics

Standard deviation15.503625
Coefficient of variation (CV)0.1870441
Kurtosis0.018236097
Mean82.887536
Median Absolute Deviation (MAD)11
Skewness0.28673645
Sum128807.23
Variance240.36238
MonotonicityNot monotonic
2026-02-28T14:48:44.964639image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
9147
 
2.9%
7643
 
2.6%
6642
 
2.6%
7142
 
2.6%
8141
 
2.5%
7740
 
2.4%
6140
 
2.4%
8439
 
2.4%
9239
 
2.4%
6539
 
2.4%
Other values (64)1142
69.7%
(Missing)85
 
5.2%
ValueCountFrequency (%)
31.721
 
0.1%
34.0471
 
0.1%
35.2434
 
0.2%
35.3171
 
0.1%
35.7931
 
0.1%
36.4541
 
0.1%
36.9951
 
0.1%
37.6021
 
0.1%
6031
1.9%
6140
2.4%
ValueCountFrequency (%)
134.0661
 
0.1%
133.8311
 
0.1%
132.1422
 
0.1%
131.561
 
0.1%
131.4251
 
0.1%
128.1651
 
0.1%
1198
0.5%
11810
0.6%
1177
0.4%
1166
0.4%
Distinct4
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size12.9 KiB
Hypertension Stage 2
680 
Hypertension Stage 1
527 
Normal
321 
Elevated
111 

Length

Max length20
Median length20
Mean length16.445394
Min length6

Characters and Unicode

Total characters26.954
Distinct characters22
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowHypertension Stage 1
2nd rowHypertension Stage 2
3rd rowNormal
4th rowHypertension Stage 1
5th rowElevated

Common Values

ValueCountFrequency (%)
Hypertension Stage 2680
41.5%
Hypertension Stage 1527
32.2%
Normal321
19.6%
Elevated111
 
6.8%

Length

2026-02-28T14:48:45.008915image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2026-02-28T14:48:45.038071image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
hypertension1207
29.8%
stage1207
29.8%
2680
16.8%
1527
13.0%
normal321
 
7.9%
elevated111
 
2.7%

Most occurring characters

ValueCountFrequency (%)
e3843
14.3%
t2525
 
9.4%
2414
 
9.0%
n2414
 
9.0%
a1639
 
6.1%
r1528
 
5.7%
o1528
 
5.7%
p1207
 
4.5%
H1207
 
4.5%
y1207
 
4.5%
Other values (12)7442
27.6%

Most occurring categories

ValueCountFrequency (%)
(unknown)26954
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e3843
14.3%
t2525
 
9.4%
2414
 
9.0%
n2414
 
9.0%
a1639
 
6.1%
r1528
 
5.7%
o1528
 
5.7%
p1207
 
4.5%
H1207
 
4.5%
y1207
 
4.5%
Other values (12)7442
27.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown)26954
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e3843
14.3%
t2525
 
9.4%
2414
 
9.0%
n2414
 
9.0%
a1639
 
6.1%
r1528
 
5.7%
o1528
 
5.7%
p1207
 
4.5%
H1207
 
4.5%
y1207
 
4.5%
Other values (12)7442
27.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown)26954
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e3843
14.3%
t2525
 
9.4%
2414
 
9.0%
n2414
 
9.0%
a1639
 
6.1%
r1528
 
5.7%
o1528
 
5.7%
p1207
 
4.5%
H1207
 
4.5%
y1207
 
4.5%
Other values (12)7442
27.6%

Estimated LDL (mg/dL)
Real number (ℝ)

High correlation  Missing 

Distinct251
Distinct (%)15.9%
Missing57
Missing (%)3.5%
Infinite0
Infinite (%)0.0%
Mean113.2359
Minimum-92.055
Maximum317.314
Zeros0
Zeros (%)0.0%
Negative16
Negative (%)1.0%
Memory size12.9 KiB
2026-02-28T14:48:45.079780image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum-92.055
5-th percentile24
Q162
median112
Q3159
95-th percentile209
Maximum317.314
Range409.369
Interquartile range (IQR)97

Descriptive statistics

Standard deviation61.435291
Coefficient of variation (CV)0.54254254
Kurtosis-0.23905254
Mean113.2359
Median Absolute Deviation (MAD)49
Skewness0.15378654
Sum179139.19
Variance3774.2949
MonotonicityNot monotonic
2026-02-28T14:48:45.128809image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
12421
 
1.3%
4019
 
1.2%
12517
 
1.0%
9017
 
1.0%
4616
 
1.0%
10315
 
0.9%
14815
 
0.9%
19315
 
0.9%
4315
 
0.9%
11715
 
0.9%
Other values (241)1417
86.5%
(Missing)57
 
3.5%
ValueCountFrequency (%)
-92.0551
0.1%
-86.9721
0.1%
-82.8821
0.1%
-79.8661
0.1%
-79.4371
0.1%
-79.1471
0.1%
-181
0.1%
-151
0.1%
-102
0.1%
-71
0.1%
ValueCountFrequency (%)
317.3141
 
0.1%
316.0711
 
0.1%
311.2461
 
0.1%
308.5141
 
0.1%
306.9211
 
0.1%
300.2276
0.4%
299.3721
 
0.1%
298.4921
 
0.1%
292.2551
 
0.1%
2372
 
0.1%

CVD Risk Score
Real number (ℝ)

High correlation  Missing 

Distinct1119
Distinct (%)69.5%
Missing29
Missing (%)1.8%
Infinite0
Infinite (%)0.0%
Mean18.227281
Minimum-20.057
Maximum114.98
Zeros0
Zeros (%)0.0%
Negative9
Negative (%)0.5%
Memory size12.9 KiB
2026-02-28T14:48:45.177367image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum-20.057
5-th percentile12.4846
Q115.15
median16.967
Q318.9
95-th percentile22.67115
Maximum114.98
Range135.037
Interquartile range (IQR)3.75

Descriptive statistics

Standard deviation10.767666
Coefficient of variation (CV)0.59074451
Kurtosis47.895992
Mean18.227281
Median Absolute Deviation (MAD)1.887
Skewness6.221455
Sum29345.923
Variance115.94264
MonotonicityNot monotonic
2026-02-28T14:48:45.234416image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
14.46
 
0.4%
16.665
 
0.3%
15.415
 
0.3%
17.955
 
0.3%
15.875
 
0.3%
14.165
 
0.3%
17.75
 
0.3%
15.255
 
0.3%
19.445
 
0.3%
16.335
 
0.3%
Other values (1109)1559
95.1%
(Missing)29
 
1.8%
ValueCountFrequency (%)
-20.0571
0.1%
-18.9771
0.1%
-18.8591
0.1%
-15.9681
0.1%
-15.7451
0.1%
-13.091
0.1%
-9.6291
0.1%
-4.1661
0.1%
-2.3191
0.1%
0.8831
0.1%
ValueCountFrequency (%)
114.981
0.1%
114.9681
0.1%
114.1431
0.1%
112.3431
0.1%
111.0081
0.1%
110.0941
0.1%
104.2711
0.1%
104.0871
0.1%
104.0021
0.1%
101.6241
0.1%

CVD Risk Level
Categorical

Distinct3
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size12.9 KiB
HIGH
793 
INTERMEDIARY
616 
LOW
230 

Length

Max length12
Median length4
Mean length6.8663819
Min length3

Characters and Unicode

Total characters11.254
Distinct characters14
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowHIGH
2nd rowINTERMEDIARY
3rd rowLOW
4th rowHIGH
5th rowHIGH

Common Values

ValueCountFrequency (%)
HIGH793
48.4%
INTERMEDIARY616
37.6%
LOW230
 
14.0%

Length

2026-02-28T14:48:45.279105image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2026-02-28T14:48:45.303533image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
high793
48.4%
intermediary616
37.6%
low230
 
14.0%

Most occurring characters

ValueCountFrequency (%)
I2025
18.0%
H1586
14.1%
E1232
10.9%
R1232
10.9%
G793
 
7.0%
N616
 
5.5%
T616
 
5.5%
M616
 
5.5%
D616
 
5.5%
A616
 
5.5%
Other values (4)1306
11.6%

Most occurring categories

ValueCountFrequency (%)
(unknown)11254
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
I2025
18.0%
H1586
14.1%
E1232
10.9%
R1232
10.9%
G793
 
7.0%
N616
 
5.5%
T616
 
5.5%
M616
 
5.5%
D616
 
5.5%
A616
 
5.5%
Other values (4)1306
11.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown)11254
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
I2025
18.0%
H1586
14.1%
E1232
10.9%
R1232
10.9%
G793
 
7.0%
N616
 
5.5%
T616
 
5.5%
M616
 
5.5%
D616
 
5.5%
A616
 
5.5%
Other values (4)1306
11.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown)11254
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
I2025
18.0%
H1586
14.1%
E1232
10.9%
R1232
10.9%
G793
 
7.0%
N616
 
5.5%
T616
 
5.5%
M616
 
5.5%
D616
 
5.5%
A616
 
5.5%
Other values (4)1306
11.6%

Interactions

2026-02-28T14:48:42.154093image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
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2026-02-28T14:48:38.307298image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-28T14:48:38.863607image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
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2026-02-28T14:48:40.438337image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-28T14:48:40.988221image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-28T14:48:41.508637image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-28T14:48:42.030731image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-28T14:48:42.631347image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-28T14:48:35.302463image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-28T14:48:36.036152image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-28T14:48:36.593905image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-28T14:48:37.108562image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-28T14:48:37.624907image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-28T14:48:38.211829image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-28T14:48:38.789451image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-28T14:48:39.366261image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-28T14:48:39.934882image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-28T14:48:40.474018image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-28T14:48:41.026670image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-28T14:48:41.541460image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-28T14:48:42.078559image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-28T14:48:42.669141image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-28T14:48:35.338739image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-28T14:48:36.073978image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-28T14:48:36.633075image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-28T14:48:37.141135image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-28T14:48:37.666098image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-28T14:48:38.261407image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-28T14:48:38.827759image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-28T14:48:39.410564image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-28T14:48:39.973367image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-28T14:48:40.510761image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-28T14:48:41.065009image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-28T14:48:41.575778image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-28T14:48:42.117155image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Correlations

2026-02-28T14:48:45.342407image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Abdominal Circumference (cm)AgeBMIBlood Pressure CategoryCVD Risk LevelCVD Risk ScoreDiabetes StatusDiastolic BPEstimated LDL (mg/dL)Family History of CVDFasting Blood Sugar (mg/dL)HDL (mg/dL)Height (cm)Height (m)Physical Activity LevelSexSmoking StatusSystolic BPTotal Cholesterol (mg/dL)Waist-to-Height RatioWeight (kg)
Abdominal Circumference (cm)1.0000.0920.0300.1010.0680.0710.1050.0690.0230.074-0.004-0.024-0.0080.0060.0450.0500.0400.0380.0330.8940.051
Age0.0921.0000.0210.0810.1470.0360.0520.0430.0020.0000.0530.0000.0420.0390.0000.0340.0900.0660.0050.052-0.008
BMI0.0300.0211.0000.0740.1240.5050.0000.0210.0250.0000.042-0.011-0.162-0.1500.0300.0380.050-0.0110.0220.0910.614
Blood Pressure Category0.1010.0810.0741.0000.0810.0730.0000.4430.0370.0000.1070.0570.1090.0990.0600.0000.0660.4400.0440.0720.000
CVD Risk Level0.0680.1470.1240.0811.0000.0760.1760.0690.1360.2290.1330.1210.1490.1450.1290.0320.2410.1340.1040.0740.097
CVD Risk Score0.0710.0360.5050.0730.0761.0000.1710.0920.4120.0650.0550.014-0.080-0.0700.0000.0000.0000.3830.4350.0990.341
Diabetes Status0.1050.0520.0000.0000.1760.1711.0000.0320.0000.0000.0720.0630.0000.0000.0630.0000.0000.0410.0000.0150.062
Diastolic BP0.0690.0430.0210.4430.0690.0920.0321.0000.0780.0950.0530.0330.0170.0130.0720.0000.0280.0410.0940.0430.004
Estimated LDL (mg/dL)0.0230.0020.0250.0370.1360.4120.0000.0781.0000.083-0.001-0.159-0.0090.0050.0000.0440.0000.0540.9230.0130.015
Family History of CVD0.0740.0000.0000.0000.2290.0650.0000.0950.0831.0000.0250.0000.0670.0870.0200.0520.0000.0000.0690.0620.000
Fasting Blood Sugar (mg/dL)-0.0040.0530.0420.1070.1330.0550.0720.053-0.0010.0251.0000.0640.0310.0340.0600.0620.0230.0610.009-0.0210.021
HDL (mg/dL)-0.0240.000-0.0110.0570.1210.0140.0630.033-0.1590.0000.0641.000-0.008-0.0240.0280.0540.0000.0290.0860.002-0.001
Height (cm)-0.0080.042-0.1620.1090.149-0.0800.0000.017-0.0090.0670.031-0.0081.0000.9680.0670.0780.0510.015-0.017-0.373-0.004
Height (m)0.0060.039-0.1500.0990.145-0.0700.0000.0130.0050.0870.034-0.0240.9681.0000.0690.0990.0000.020-0.004-0.3690.005
Physical Activity Level0.0450.0000.0300.0600.1290.0000.0630.0720.0000.0200.0600.0280.0670.0691.0000.0000.0000.0330.0390.0280.035
Sex0.0500.0340.0380.0000.0320.0000.0000.0000.0440.0520.0620.0540.0780.0990.0001.0000.0000.0000.0000.0500.079
Smoking Status0.0400.0900.0500.0660.2410.0000.0000.0280.0000.0000.0230.0000.0510.0000.0000.0001.0000.0000.0000.0230.034
Systolic BP0.0380.066-0.0110.4400.1340.3830.0410.0410.0540.0000.0610.0290.0150.0200.0330.0000.0001.0000.0570.018-0.006
Total Cholesterol (mg/dL)0.0330.0050.0220.0440.1040.4350.0000.0940.9230.0690.0090.086-0.017-0.0040.0390.0000.0000.0571.0000.0320.023
Waist-to-Height Ratio0.8940.0520.0910.0720.0740.0990.0150.0430.0130.062-0.0210.002-0.373-0.3690.0280.0500.0230.0180.0321.0000.053
Weight (kg)0.051-0.0080.6140.0000.0970.3410.0620.0040.0150.0000.021-0.001-0.0040.0050.0350.0790.034-0.0060.0230.0531.000

Missing values

2026-02-28T14:48:42.739104image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
A simple visualization of nullity by column.
2026-02-28T14:48:42.824171image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2026-02-28T14:48:42.923434image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

Patient IDDate of ServiceSexAgeWeight (kg)Height (m)BMIAbdominal Circumference (cm)Blood Pressure (mmHg)Total Cholesterol (mg/dL)HDL (mg/dL)Fasting Blood Sugar (mg/dL)Smoking StatusDiabetes StatusPhysical Activity LevelFamily History of CVDHeight (cm)Waist-to-Height RatioSystolic BPDiastolic BPBlood Pressure CategoryEstimated LDL (mg/dL)CVD Risk ScoreCVD Risk Level
0isDx5313November 08, 2023M44.000114.3001.72038.600100.000112/83228.077.091.0YYHighN172.0000.581112.083.0Hypertension Stage 1121.019.880HIGH
1LHCK296120/03/2024F57.00092.9231.84233.116106.315101/91158.071.076.0NYHighY184.1720.577101.091.0Hypertension Stage 257.016.833INTERMEDIARY
2WjVn16992021-05-27FNaN73.4001.65027.00078.10090/74135.060.0150.0NNHighN165.0000.47390.074.0Normal45.012.600LOW
3dCDO1109April 18, 2022F35.000113.3001.78035.80079.60092/89158.034.0111.0YNModerateY178.0000.44792.089.0Hypertension Stage 194.014.920HIGH
4pnpE108001/11/2024F48.000102.2001.75033.400106.700121/68207.049.0147.0YYLowY175.0000.610121.068.0Elevated128.018.870HIGH
5MQyB274725 Mar 24M43.00052.7001.85015.400107.700107/61105.032.070.0YNHighN185.0000.582107.061.0Normal43.010.530INTERMEDIARY
6DHdn896822 May 25F31.00087.0001.66031.60091.500139/81207.056.082.0NNLowY166.0000.551139.081.0Hypertension Stage 1121.017.410HIGH
7vkQL9700October 26, 2023M69.00059.6841.94023.914117.986106/115206.042.0140.0YYHighY193.9810.608106.0115.0Hypertension Stage 2134.016.203HIGH
8bUBT999415/12/2023F89.16285.6001.66031.10083.600103/99222.066.099.0YYHighN166.0000.504103.099.0Hypertension Stage 2126.0NaNHIGH
9nktq6689January 16, 2022F57.000100.1301.84022.24280.814165/99123.054.094.0NNLowN183.9880.439165.099.0Hypertension Stage 239.015.158LOW
Patient IDDate of ServiceSexAgeWeight (kg)Height (m)BMIAbdominal Circumference (cm)Blood Pressure (mmHg)Total Cholesterol (mg/dL)HDL (mg/dL)Fasting Blood Sugar (mg/dL)Smoking StatusDiabetes StatusPhysical Activity LevelFamily History of CVDHeight (cm)Waist-to-Height RatioSystolic BPDiastolic BPBlood Pressure CategoryEstimated LDL (mg/dL)CVD Risk ScoreCVD Risk Level
1629dvpf13992024-11-25M35.050.4001.67018.100108.800133/71NaN39.0144.0YNModerateY167.0000.651133.071.0Hypertension Stage 1188.015.410HIGH
1630EBtC326703-31-2025F48.059.4001.88016.80091.200102/84214.069.077.0NNLowY188.0000.485102.084.0Hypertension Stage 1115.012.740HIGH
1631pWUu217909/11/2025M45.064.4201.92128.51381.261131/115219.074.0157.0NYLowY192.1220.423131.0115.0Hypertension Stage 1115.018.633INTERMEDIARY
1632ioby218313/08/2024M40.0120.000NaN35.400100.90094/68223.062.084.0YYModerateY184.0000.54894.068.0Normal131.018.240HIGH
1633gBFe424904/02/2020F52.089.7001.88025.400107.500145/92142.0NaN96.0YYLowY188.0000.572145.092.0Hypertension Stage 242.017.170HIGH
1634mrzf5858May 21, 2021F35.077.6001.78024.50084.600124/90143.076.0108.0NNLowN178.0000.475124.090.0Hypertension Stage 237.013.960LOW
1635nPnN547712/04/2022F35.092.0051.726NaN98.69295/111156.080.080.0NYHighN172.6020.57295.0111.0Hypertension Stage 246.014.316LOW
1636ePpS471012/04/2022M48.050.1001.77016.000104.100146/95210.070.0108.0NNLowY177.0000.588146.0NaNHypertension Stage 2110.0NaNHIGH
1637QSFT6794September 06, 2025M49.0NaN1.63023.10093.800144/91191.079.0117.0YYModerateY163.0000.575144.0NaNHypertension Stage 282.017.640HIGH
1638pDkH84322021-05-01FNaN59.2001.80018.30071.900116/83280.079.084.0NNHighN180.0000.399116.083.0Hypertension Stage 1171.015.060INTERMEDIARY

Duplicate rows

Most frequently occurring

Patient IDDate of ServiceSexAgeWeight (kg)Height (m)BMIAbdominal Circumference (cm)Blood Pressure (mmHg)Total Cholesterol (mg/dL)HDL (mg/dL)Fasting Blood Sugar (mg/dL)Smoking StatusDiabetes StatusPhysical Activity LevelFamily History of CVDHeight (cm)Waist-to-Height RatioSystolic BPDiastolic BPBlood Pressure CategoryEstimated LDL (mg/dL)CVD Risk ScoreCVD Risk Level# duplicates
0AhYt134609-28-2020M41.0071.3001.73023.80107.900139/61253.077.094.0YNLowY173.000NaN139.0NaNHypertension Stage 1146.00016.770HIGH2
1BQvQ643109/11/2020M33.00118.3001.69041.4072.100116/93171.044.0145.0NNModerateN210.5540.427116.093.0Hypertension Stage 297.00017.500LOW2
2CDsa265123/06/2025M39.0073.3001.74024.2095.000111/84158.037.081.0NYHighY174.0000.546111.084.0Hypertension Stage 191.00015.550INTERMEDIARY2
3CKKa5109March 18, 2023M51.0085.9001.78027.1087.200144/70189.044.0100.0NNLowN178.0000.490144.070.0Hypertension Stage 2300.22716.400INTERMEDIARY2
4CYeS296526 Jul 25M33.00108.7001.860NaN96.900130/95250.047.0106.0NNLowYNaN0.521130.0NaNHypertension Stage 1173.00017.780HIGH2
5CeLz474331/03/2022M34.0054.0001.610NaN96.700116/72253.037.0NaNNNHighY161.0000.601116.072.0Normal186.00015.020INTERMEDIARY2
6ChGR77792022-08-29M46.0078.7001.640NaN105.500137/76196.037.0102.0NNHighY164.0000.643137.076.0Hypertension Stage 1129.00016.630HIGH2
7DIVT312117 Aug 23M36.00106.2001.76034.3074.800124/66171.048.0141.0YNHighN176.0000.425124.066.0Elevated93.00016.480INTERMEDIARY2
8DYFB4591February 16, 2025MNaN52.8001.80016.3077.300113/96167.032.0135.0NYLowN180.0000.429113.096.0Hypertension Stage 2105.00014.250INTERMEDIARY2
9DhUJ72392020-01-24M6.4269.7691.69622.0674.184120/76197.065.0151.0YNLowN169.5600.438120.076.0ElevatedNaN14.352INTERMEDIARY2